Human genomic variability occurs at different scales, from single nucleotide polymorphisms to DNA segments containing a large number of genes. Copy number variations (CNVs) represent a significant part of human genetic heterogeneity and have also been associated with a wide range of diseases and disorders. Although large CNVs may be detectable in noisy array-based data, short, localized aberrations may be undetectable due to low signal-to-noise ratio (SNR). Short CNVs may, however, play an important role in human disease, and thus highly sensitive methodologies are needed for their detection. For meaningful identification of disease CNVs, it is necessary to first estimate the locations and levels of normal allelic aberrations for baseline comparison. We have successfully developed a signal processing-based methodology for sequence denoising followed by pattern matching, to increase SNR in normal genomic data and improve CNV detection in normals. We propose to further develop this method for normals, and then to develop and extend it for application in the cancer setting, in particular, for atypical meningioma and lung squamous cell carcinoma, for highly sensitive and specific detection of cancer related CNV's. CNV detection in cancer is critical for understanding the etiology of disease and ultimately for the development of therapeutic targets, and our methodology will contribute uniquely and substantially to this goal. We will use The Cancer Genome Atlas resource of the National Cancer Institute for normal array data and for the lung cancer data.